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Title: Trelliscope: A System for Detailed Visualization in Analysis of Large Complex Data

Visualization plays a critical role in the statistical model building and data analysis process. Data analysts, well-versed in statistical and machine learning methods, visualize data to hypothesize and validate models. These analysts need flexible, scalable visualization tools that are not decoupled from their analysis environment. In this paper we introduce Trelliscope, a visualization framework for statistical analysis of large complex data. Trelliscope extends Trellis, an effective visualization framework that divides data into subsets and applies a plotting method to each subset, arranging the results in rows and columns of panels. Trelliscope provides a way to create, arrange and interactively view panels for very large datasets, enabling flexible detailed visualization for data of any size. Scalability is achieved using distributed computing technologies coupled with . We discuss the underlying principles, design, and scalable architecture of Trelliscope, and illustrate its use on three analysis projects in the domains of proteomics, high energy physics, and power systems engineering.
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Conference: IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV 2013), October 13-14, 2013, Atlanta, Georgia, 105-112
IEEE, Piscataway, NJ, United States(US).
Research Org:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
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Country of Publication:
United States